





import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowerCAmelCase_        =							logging.getLogger(__name__)

if __name__ == "__main__":
	lowerCAmelCase_        =							argparse.ArgumentParser(
	    description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
	)
	parser.add_argument(
	    '''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
	)
	parser.add_argument(
	    '''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
	)
	parser.add_argument('''--vocab_size''', default=3_0_5_2_2, type=int)
	lowerCAmelCase_        =							parser.parse_args()

	logger.info(F'''Loading data from {args.data_file}''')
	with open(args.data_file, '''rb''') as fp:
		lowerCAmelCase_        =							pickle.load(fp)

	logger.info('''Counting occurrences for MLM.''')
	lowerCAmelCase_        =							Counter()
	for tk_ids in data:
		counter.update(tk_ids)
	lowerCAmelCase_        =							[0] * args.vocab_size
	for k, v in counter.items():
		lowerCAmelCase_        =							v

	logger.info(F'''Dump to {args.token_counts_dump}''')
	with open(args.token_counts_dump, '''wb''') as handle:
		pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)








"""simple docstring"""





import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
_SCREAMING_SNAKE_CASE   :							int          =		pd.read_csv(
    '''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
    '''position_salaries.csv'''
)
_SCREAMING_SNAKE_CASE   :							List[Any]          =		dataset.iloc[:, 1:2].values
_SCREAMING_SNAKE_CASE   :							str          =		dataset.iloc[:, 2].values


_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE   :							Dict          =		train_test_split(X, y, test_size=0.2, random_state=0)


_SCREAMING_SNAKE_CASE   :							Optional[int]          =		PolynomialFeatures(degree=4)
_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		poly_reg.fit_transform(X)
_SCREAMING_SNAKE_CASE   :							List[str]          =		LinearRegression()
pol_reg.fit(X_poly, y)







def 	lowerCamelCase__			(				) ->							List[str]:
  plt.scatter(_lowerCamelCase						,							_lowerCamelCase						,							color='red'					)
  plt.plot(_lowerCamelCase						,							pol_reg.predict(poly_reg.fit_transform(_lowerCamelCase					)					)						,							color='blue'					)
  plt.title('Truth or Bluff (Linear Regression)'					)
  plt.xlabel('Position level'					)
  plt.ylabel('Salary'					)
  plt.show()


if __name__ == "__main__":
  viz_polymonial()

  # Predicting a new result with Polymonial Regression
  pol_reg.predict(poly_reg.fit_transform([[5.5]]))
  # output should be 132148.43750003




